code: def pad_obses(self, *, expand_mult=1):
      pad_h = np.ceil(self.patch_h * expand_mult).astype(int)
      pad_w = np.ceil(self.patch_w * expand_mult).astype(int)
      if pad_h > self.pad_h or pad_w > self.pad_w:
          self.pad_h = pad_h
          self.pad_w = pad_w
          self.padded_obses = (
              np.indices(
                  (
                      self.obses_full.shape[1] + self.pad_h * 2,
                      self.obses_full.shape[2] + self.pad_w * 2,
                  )
              ).sum(axis=0)
              % 2
          )
          self.padded_obses = self.padded_obses * 0.25 + 0.75
          self.padded_obses = self.padded_obses.astype(
              self.obses_full.dtype
          )
          self.padded_obses = self.padded_obses[None, ..., None]
          self.padded_obses = self.padded_obses.repeat(
              self.obses_full.shape[0], axis=0
          )
          self.padded_obses = self.padded_obses.repeat(3, axis=-1)
          self.padded_obses[
              :, self.pad_h : -self.pad_h, self.pad_w : -self.pad_w, :
          ] = self.obses_full
  
file_path: lucid/lucid/scratch/rl_util/nmf.py
imports: import numpy
  from functools import reduce
  import scipy.ndimage
  from lucid.misc.channel_reducer import ChannelReducer
  import lucid.optvis.param
  import lucid.optvis.objectives
  import lucid.optvis.render
  import lucid.optvis.transform
  from .attribution import get_acts, get_attr
label_desc: Use image node_id 3 for calling the LayerNMF obj's underlying member method code with example usage: obj.pad_obses() without return types